ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering

Denis Kotkov, Zhao Qian, Launis Kati, Mats Neovius

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

7 Lataukset (Pure)


Related item recommendations have a long history in recommender systems, but they tend to be a static list of similar items with respect to a target item of interest without any support of user control. In this paper, we propose ClusterExplorer, a novel approach for enabling user control over related recommendations. The approach allows users to explore the latent space of user-item interactions through controlling related recommendations. We evaluated ClusterExplorer in the book domain with 42 participants recruited in a public library and found that our approach has higher user satisfaction of browsing items and is more helpful in finding interesting items compared to traditional related item recommendations.

OtsikkoRecSys '20: Fourteenth ACM Conference on Recommender Systems
KustantajaAssociation for Computing Machinery
ISBN (elektroninen)9781450375832
ISBN (painettu)9781450375832
DOI - pysyväislinkit
TilaJulkaistu - 22 syyskuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
Tapahtumaconference; 2020-09-22; 2020-09-26 - Fourteenth ACM Conference on Recommender Systems
Kesto: 22 syyskuuta 202026 syyskuuta 2020


Konferenssiconference; 2020-09-22; 2020-09-26


  • conversational recommender systems
  • critiquing recommender systems
  • information exploration tool
  • interactive recommendation
  • recommender systems
  • related item recommendations
  • user control
  • user interfaces


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